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Fast computational optimization of TMS coil placement for individualized electric field targeting
bioRxiv - Bioengineering Pub Date : 2020-06-23 , DOI: 10.1101/2020.05.27.120022
Luis J. Gomez , Moritz Dannhauer , Angel V. Peterchev

Background: During transcranial magnetic stimulation (TMS) a coil placed on the scalp is used to non-invasively modulate activity of targeted brain networks via a magnetically induced electric field (E-field). Ideally, the E-field induced during TMS is concentrated on a targeted cortical region of interest (ROI). Objective: To improve the accuracy of TMS we have developed a fast computational auxiliary dipole method (ADM) for determining the optimum coil position and orientation. The optimum coil placement maximizes the E-field along a predetermined direction or, alternatively, the overall E-field magnitude in the targeted ROI. Furthermore, ADM can assess E-field uncertainty resulting from precision limitations of TMS coil placement protocols. Method: ADM leverages the electromagnetic reciprocity principle to compute rapidly the TMS induced E-field in the ROI by using the E-field generated by a virtual constant current source residing in the ROI. The framework starts by solving for the conduction currents resulting from this ROI current source. Then, it rapidly determines the average E-field induced in the ROI for each coil position by using the conduction currents and a fast-multipole method. To further speed-up the computations, the coil is approximated using auxiliary dipoles enabling it to represent all coil orientations for a given coil position with less than 600 dipoles. Results: Using ADM, the E-fields generated in an MRI-derived head model when the coil is placed at 5,900 different scalp positions and 360 coil orientations per position (over 2.1 million unique configurations) can be determined in under 15 minutes on a standard laptop computer. This enables rapid extraction of the optimum coil position and orientation as well as the E-field variation resulting from coil positioning uncertainty. Conclusion: ADM enables the rapid determination of coil placement that maximizes E-field delivery to a specific brain target. This method can find the optimum coil placement in under 15 minutes enabling its routine use for TMS. Furthermore, it enables the fast quantification of uncertainty in the induced E-field due to limited precision of TMS coil placement protocols, enabling minimization and statistical analysis of the E-field dose variability.

中文翻译:

针对个性化电场目标的TMS线圈放置的快速计算优化

背景:在经颅磁刺激(TMS)期间,放置在头皮上的线圈用于通过磁感应电场(电场)非侵入性地调节目标脑网络的活动。理想情况下,在TMS期间诱发的电场集中在目标皮层感兴趣区域(ROI)上。目的:为提高TMS的准确性,我们开发了一种快速计算辅助偶极子方法(ADM),用于确定最佳线圈位置和方向。最佳的线圈放置使沿预定方向的电场最大化,或者使目标ROI中的整体电场幅度最大化。此外,ADM可以评估由TMS线圈放置协议的精度限制引起的电场不确定性。方法:ADM利用电磁互易原理,通过使用由驻留在ROI中的虚拟恒定电流源生成的E场,快速计算ROI中TMS感应的E场。该框架首先要解决由该ROI电流源产生的传导电流。然后,通过使用传导电流和快速多极方法,快速确定每个线圈位置在ROI中感应的平均电场。为了进一步加快计算速度,使用辅助偶极子对线圈进行了逼近,使其能够以小于600的偶极子表示给定线圈位置的所有线圈方向。结果:使用ADM,当将线圈放置在5,900个不同的头皮位置和每个位置360个线圈方向(超过2个)时,在MRI衍生的头部模型中产生的电场。在一台标准笔记本电脑上,不到15分钟即可确定100万种独特配置。这样可以快速提取出最佳的线圈位置和方向,以及由于线圈定位不确定性而产生的电场变化。结论:ADM能够快速确定线圈位置,从而最大程度地将电场传输到特定的大脑目标。这种方法可以在15分钟内找到最佳的线圈放置,从而使其能够常规用于TMS。此外,由于TMS线圈放置协议的精度有限,它可以快速量化感应电场的不确定性,从而可以最小化和统计分析电场剂量的可变性。这样可以快速提取出最佳的线圈位置和方向,以及由于线圈定位不确定性而产生的电场变化。结论:ADM能够快速确定线圈位置,从而最大程度地将电场传输到特定的大脑目标。这种方法可以在15分钟内找到最佳的线圈放置,从而使其能够常规用于TMS。此外,由于TMS线圈放置协议的精度有限,它可以快速量化感应电场的不确定性,从而可以最小化和统计分析电场剂量的可变性。这样可以快速提取出最佳的线圈位置和方向,以及由于线圈定位不确定性而产生的电场变化。结论:ADM能够快速确定线圈位置,从而最大程度地将电场传输到特定的大脑目标。这种方法可以在15分钟内找到最佳的线圈放置,从而使其能够常规用于TMS。此外,由于TMS线圈放置协议的精度有限,它可以快速量化感应电场的不确定性,从而可以最小化和统计分析电场剂量的可变性。这种方法可以在15分钟内找到最佳的线圈放置,从而使其能够常规用于TMS。此外,由于TMS线圈放置协议的精度有限,它可以快速量化感应电场的不确定性,从而可以最小化和统计分析电场剂量的可变性。这种方法可以在15分钟内找到最佳的线圈放置,从而使其能够常规用于TMS。此外,由于TMS线圈放置协议的精度有限,它可以快速量化感应电场的不确定性,从而可以最小化和统计分析电场剂量的可变性。
更新日期:2020-06-24
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